Adaptively Approximate Techniques in Distributed
                              Architectures

                                                      Barbara Catania
                                                     University of Genoa
                                                   barbara.catania@unige.it



    The wealth of information generated by users interacting with the network and its applications is often under-
utilized due to complications in accessing heterogeneous and dynamic data and in retrieving relevant information
from sources having possibly unknown formats and structures. Processing complex requests on such information
sources is, thus, costly, though not guaranteeing user satisfaction.
    In such environments, requests are often relaxed and query processing is forced to be adaptive and approximate,
either to cope with limited processing resources (QoS-oriented techniques), possibly at the price of sacrificing result
quality, or to cope with limited data knowledge and data heterogeneity (QoD-oriented techniques), with the aim of
improving the quality of results. While both kinds of approximation techniques have been proposed, most adaptive
solutions are QoS-oriented.
    Additionally, techniques which apply a QoD-oriented approximation in a QoD-oriented adaptive way (called adap-
tively approximate techniques), though demonstrated potentially useful in getting the right compromise between pre-
cise and approximate computations, have been largely neglected. In this talk, after presenting and classifying several
approximate and/or adaptive query processing approaches, proposed for different distributed architectures, we show,
with some concrete examples, the benefits of using adaptively approximate techniques. We then present the result of
our ongoing research in the context of data stream and geo-social data management.




Proceedings of the Tenth Spring Researcher’s Colloquium on Database and Information Systems, Veliky Novgorod, Russia, 2014